Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
aHUGIN: a system creating adaptive causal probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Network Refinement Via Machine Learning Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Random Generation of Bayesian Networks
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Analysis in HUGIN of data conflict
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Identifying Markov Blankets with Decision Tree Induction
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Using new data to refine a Bayesian network
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
An adaptive prequential learning framework for bayesian network classifiers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Alert systems for production plants: a methodology based on conflict analysis
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Scalable, efficient and correct learning of markov boundaries under the faithfulness assumption
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
iMMPC: a local search approach for incremental Bayesian network structure learning
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN is gradually being constructed as observations of the environment are made. Existing algorithms for incremental learning assume that the samples in the database have been drawn from a single underlying distribution. In this paper we relax this assumption, so that the underlying distribution can change during the sampling of the database. The proposed method can thus be used in unknown environments, where it is not even known whether the dynamics of the environment are stable. We state formal correctness results for our method, and demonstrate its feasibility experimentally.